The ability of physicians to predict hyperkalemia from the ECG

Validation of Noninvasive Detection of Hyperkalemia by Artificial Intelligence-Enhanced Electrocardiography in High Acuity Settings

Background

Artificial intelligence (AI) electrocardiogram (ECG) analysis can enable detection of hyperkalemia. In this validation, we assessed the algorithm's performance in two high acuity settings.

Methods

An emergency department (ED) cohort (February to August 2021) and a mixed intensive care unit (ICU) cohort (August 2017 to February 2018) were identified and analyzed separately. For each group, pairs of laboratory-collected potassium and 12 lead ECGs obtained within 4 hours of each other were identified. The previously developed AI ECG algorithm was subsequently applied to leads 1 and 2 of the 12 lead ECGs to screen for hyperkalemia (potassium >6.0 mEq/L).

Results

The ED cohort (N=40,128) had a mean age of 60 years, 48% were male, and 1% (N=351) had hyperkalemia. The area under the curve (AUC) of the AI-enhanced ECG (AI-ECG) to detect hyperkalemia was 0.88, with sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and positive likelihood ratio (LR+) of 80%, 80%, 3%, 99.8%, and 4.0, respectively, in the ED cohort. Low-eGFR (<30 ml/min) subanalysis yielded AUC, sensitivity, specificity, PPV, NPV, and LR+ of 0.83, 86%, 60%, 15%, 98%, and 2.2, respectively, in the ED cohort. The ICU cohort (N=2636) had a mean age of 65 years, 60% were male, and 3% (N=87) had hyperkalemia. The AUC for the AI-ECG was 0.88 and yielded sensitivity, specificity, PPV, NPV, and LR+ of 82%, 82%, 14%, 99%, and 4.6, respectively in the ICU cohort. Low-eGFR subanalysis yielded AUC, sensitivity, specificity, PPV, NPV, and LR+ of 0.85, 88%, 67%, 29%, 97%, and 2.7, respectively in the ICU cohort.

Conclusions

The AI-ECG algorithm demonstrated a high NPV, suggesting that it is useful for ruling out hyperkalemia, but a low PPV, suggesting that it is insufficient for treating hyperkalemia.

Electrocardiographic Abnormalities in Patients with Hyperkalemia: A Retrospective Study in an Emergency Department in Colombia

Introduction

Hyperkalemia is a prevalent electrolyte disorder related to elevated serum potassium levels, resulting in diverse abnormal electrocardiographic findings and associated clinical signs and symptoms, often necessitating specific treatment. However, in some patients, these abnormal findings may not be present on the electrocardiogram even in elevated serum potassium levels. This study aims to identify electrocardiographic abnormalities related to the severity of hyperkalemia and the clinical outcomes in an emergency department in southwestern Colombia.

Methodology

This is a retrospective cross-sectional descriptive study. We described the electrocardiographic findings, clinical characteristics, treatment, and outcomes related to the degrees of hyperkalemia. The potential association between the severity of hyperkalemia and electrocardiographic findings was evaluated.

Results

A total of 494 patients were included. The median of the potassium level was 6.6 mEq/L. Abnormal electrocardiographic findings were reported in 61.5% of the cases. Mild and severe hyperkalemia groups reported abnormalities in 59.9% and 61.2%, respectively. The most common electrocardiography abnormalities were the peaked T wave 36.2%, followed by wide QRS 83 (16.8%). Only 1.4% of patients had adverse outcomes. The abnormal findings were registered in 61.5%. Mortality was 11.9%. The peaked T wave was the most common finding across different levels of hyperkalemia severity.

Conclusion

High serum potassium levels are related with abnormal ECG. However, patients with different degrees of hyperkalemia could not describe abnormal ECG findings. In a high proportion of patients with renal chronic disease and hyperkalemia, the abnormalities in the ECG could be minimal or absent.

A Wide Wide QRS

This case report describes a patient in their 70s with shortness of breath and generalized weakness and a history of coronary artery bypass surgery, ischemic cardiomyopathy, a dual-chamber defibrillator, paroxysmal atrial fibrillation, and kidney failure while undergoing hemodialysis.

Hyperkalemia Detection in Emergency Departments Using Initial ECGs: A Smartphone AI ECG Analyzer vs. Board-Certified Physicians

BACKGROUND

Hyperkalemia is a potentially fatal condition that mandates rapid identification in emergency departments (EDs). Although a 12-lead electrocardiogram (ECG) can indicate hyperkalemia, subtle changes in the ECG often pose detection challenges. An artificial intelligence application that accurately assesses hyperkalemia risk from ECGs could revolutionize patient screening and treatment. We aimed to evaluate the efficacy and reliability of a smartphone application, which utilizes camera-captured ECG images, in quantifying hyperkalemia risk compared to human experts.

METHODS

We performed a retrospective analysis of ED hyperkalemic patients (serum potassium ≥ 6 mmol/L) and their age- and sex-matched non-hyperkalemic controls. The application was tested by five users and its performance was compared to five board-certified emergency physicians (EPs).

RESULTS

Our study included 125 patients. The area under the curve (AUC)-receiver operating characteristic of the application's output was nearly identical among the users, ranging from 0.898 to 0.904 (median: 0.902), indicating almost perfect interrater agreement (Fleiss' kappa 0.948). The application demonstrated high sensitivity (0.797), specificity (0.934), negative predictive value (NPV) (0.815), and positive predictive value (PPV) (0.927). In contrast, the EPs showed moderate interrater agreement (Fleiss' kappa 0.551), and their consensus score had a significantly lower AUC of 0.662. The physicians' consensus demonstrated a sensitivity of 0.203, specificity of 0.934, NPV of 0.527, and PPV of 0.765. Notably, this performance difference remained significant regardless of patients' sex and age (P < 0.001 for both).

CONCLUSION

Our findings suggest that a smartphone application can accurately and reliably quantify hyperkalemia risk using initial ECGs in the ED.

Disorders of Potassium

Abnormalities in serum potassium are commonly encountered in patients presenting to the emergency department. A variety of acute and chronic causes can lead to life-threatening illness in both hyperkalemia and hypokalemia. Here we summarize the relevant causes, risks, and treatment options for these frequently encountered disorders.

Emergency Department Presentation of Life-threatening Symptomatic Hyperkalemia From an Angiotensin Receptor Blocker in a Low-risk Individual

Hyperkalemia is a common electrolyte abnormality with characteristic electrocardiogram changes. Both angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) increase the risk of developing hyperkalemia. This case highlights a rare life-threatening episode of hyperkalemia in an individual whose only risk factor was an ARB. A 58-year-old female presented with sudden-onset chest pressure, light-headedness, and diaphoresis. Her initial electrocardiogram showed a nearly sinusoidal rhythm with a widened ventricular depolarization (QRS) and prolonged QT-interval (QTc). Life-threatening hyperkalemia of 9.1 mmol/L was confirmed with a rapid point-of-care electrolyte panel. She was rapidly treated with calcium, potassium-shifting and eliminating medications, and emergent hemodialysis. After stabilization, a thorough workup found that the patient's only risk factor for hyperkalemia was her use of an ARB. While both ARBs and ACEIs are commonly associated with mild hyperkalemia, life-threatening hyperkalemia is rare, particularly in patients without concomitant renal failure, diabetes mellitus, adrenal disease, or potassium-sparing diuretic use. However, this case illustrates that life-threatening hyperkalemia is possible in patients solely taking an ARB without prior significant risk factors. Despite normal renal function in an individual without heart failure or diabetes, this patient developed life-threatening hyperkalemia.

Crush injury and syndrome: A review for emergency clinicians

INTRODUCTION

Primary disasters may result in mass casualty events with serious injuries, including crush injury and crush syndrome.

OBJECTIVE

This narrative review provides a focused overview of crush injury and crush syndrome for emergency clinicians.

DISCUSSION

Millions of people worldwide annually face natural or human-made disasters, which may lead to mass casualty events and severe medical issues including crush injury and syndrome. Crush injury is due to direct physical trauma and compression of the human body, most commonly involving the lower extremities. It may result in asphyxia, severe orthopedic injury, compartment syndrome, hypotension, and organ injury (including acute kidney injury). Crush syndrome is the systemic manifestation of severe, traumatic muscle injury. Emergency clinicians are at the forefront of the evaluation and treatment of these patients. Care at the incident scene is essential and focuses on treating life-threatening injuries, extrication, triage, fluid resuscitation, and transport. Care at the healthcare facility includes initial stabilization and trauma evaluation as well as treatment of any complication (e.g., compartment syndrome, hyperkalemia, rhabdomyolysis, acute kidney injury).

CONCLUSIONS

Crush injury and crush syndrome are common in natural and human-made disasters. Emergency clinicians must understand the pathophysiology, evaluation, and management of these conditions to optimize patient care.

Using Deep Transfer Learning to Detect Hyperkalemia From Ambulatory Electrocardiogram Monitors in Intensive Care Units: Personalized Medicine Approach

BACKGROUND

Hyperkalemia is a critical condition, especially in intensive care units. So far, there have been no accurate and noninvasive methods for recognizing hyperkalemia events on ambulatory electrocardiogram monitors.

OBJECTIVE

This study aimed to improve the accuracy of hyperkalemia predictions from ambulatory electrocardiogram (ECG) monitors using a personalized transfer learning method; this would be done by training a generic model and refining it with personal data.

METHODS

This retrospective cohort study used open source data from the Waveform Database Matched Subset of the Medical Information Mart From Intensive Care III (MIMIC-III). We included patients with multiple serum potassium test results and matched ECG data from the MIMIC-III database. A 1D convolutional neural network-based deep learning model was first developed to predict hyperkalemia in a generic population. Once the model achieved a state-of-the-art performance, it was used in an active transfer learning process to perform patient-adaptive heartbeat classification tasks.

RESULTS

The results show that by acquiring data from each new patient, the personalized model can improve the accuracy of hyperkalemia detection significantly, from an average of 0.604 (SD 0.211) to 0.980 (SD 0.078), when compared with the generic model. Moreover, the area under the receiver operating characteristic curve level improved from 0.729 (SD 0.240) to 0.945 (SD 0.094).

CONCLUSIONS

By using the deep transfer learning method, we were able to build a clinical standard model for hyperkalemia detection using ambulatory ECG monitors. These findings could potentially be extended to applications that continuously monitor one's ECGs for early alerts of hyperkalemia and help avoid unnecessary blood tests.

Treatment of Presumed Hyperkalemia in the Prehospital Setting

INTRODUCTION

Hyperkalemia (HK) is common and potentially a life-threatening condition. If untreated, HK can progress to ventricular arrhythmia and cardiac arrest. Early treatment reduces mortality in HK. This study evaluates a novel protocol for identification and empiric management of presumed HK in the prehospital setting.

METHODS

This was a retrospective, observational chart review of a single, large, suburban Emergency Medical Services (EMS) system. Patients treated for presumed HK, with both a clinical concern for HK and electrocardiogram (ECG) changes consistent with HK, from February 2018 through February 2021 were eligible for inclusion. Patients were excluded if found to be in cardiac arrest on EMS arrival. Empiric treatment of HK included administration of calcium, sodium bicarbonate, and albuterol. Post-treatment, patients were placed on cardiac monitoring and adverse events recorded enroute to receiving hospital. Protocol compliance was assessed by two independent reviewers. Serum potassium (K) level was obtained from hospital medical records.

RESULTS

A total of 582 patients were treated for HK, of which 533 patients were excluded due to cardiac arrest prior to EMS arrival. The remaining 48 patients included in the analysis had a mean age of 56 (SD = 20) years and were 60.4% (n = 29) male with 77.1% (n = 37) Caucasian, 10.4% (n = 5) African American, and 12.5% (n = 6) Hispanic. Initial blood draw at the receiving facilities showed K >5.0mEq/L in 22 (45.8%), K of 3.5-5.0mEq/L in 23 (47.9%), and K <3.5mEq/L in three patients (6.3%). Independent review of the EMS ECG found the presence of hyperkalemic-related change in 43 (89.6%) cases, and five (10.4%) patients did not meet criteria for treatment due to lack of either appropriate ECG findings or clinical suspicion. No episodes of unstable tachyarrhythmia or cardiac arrest occurred during EMS treatment or transport.

CONCLUSION

The study evaluated a novel protocol for detecting and managing HK in the prehospital setting. It is feasible for EMS crews to administer this protocol, although a larger study is needed to make the results generalizable.

The conundrum of the complex relationship between acute kidney injury and cardiac arrhythmias

Acute kidney injury (AKI) is defined by a rapid increase in serum creatinine levels, reduced urine output, or both. Death may occur in 16%-49% of patients admitted to an intensive care unit with severe AKI. Complex arrhythmias are a potentially serious complication in AKI patients with pre-existing or AKI-induced heart damage and myocardial dysfunction, fluid overload, and especially electrolyte and acid-base disorders representing the pathogenetic mechanisms of arrhythmogenesis. Cardiac arrhythmias, in turn, increase the risk of poor renal outcomes, including AKI. Arrhythmic risk in AKI patients receiving kidney replacement treatment may be reduced by modifying dialysis/replacement fluid composition. The most common arrhythmia observed in AKI patients is atrial fibrillation. Severe hyperkalemia, sometimes combined with hypocalcemia, causes severe bradyarrhythmias in this clinical setting. Although the likelihood of life-threatening ventricular arrhythmias is reportedly low, the combination of cardiac ischemia and specific electrolyte or acid-base abnormalities may increase this risk, particularly in AKI patients who require kidney replacement treatment. The purpose of this review is to summarize the available epidemiological, pathophysiological, and prognostic evidence aiming to clarify the complex relationships between AKI and cardiac arrhythmias.

Noninvasive Screening Tool for Hyperkalemia Using a Single-Lead Electrocardiogram and Deep Learning: Development and Usability Study

Background

Hyperkalemia monitoring is very important in patients with chronic kidney disease (CKD) in emergency medicine. Currently, blood testing is regarded as the standard way to diagnose hyperkalemia (ie, using serum potassium levels). Therefore, an alternative and noninvasive method is required for real-time monitoring of hyperkalemia in the emergency medicine department.

Objective

This study aimed to propose a novel method for noninvasive screening of hyperkalemia using a single-lead electrocardiogram (ECG) based on a deep learning model.

Methods

For this study, 2958 patients with hyperkalemia events from July 2009 to June 2019 were enrolled at 1 regional emergency center, of which 1790 were diagnosed with chronic renal failure before hyperkalemic events. Patients who did not have biochemical electrolyte tests corresponding to the original 12-lead ECG signal were excluded. We used data from 855 patients (555 patients with CKD, and 300 patients without CKD). The 12-lead ECG signal was collected at the time of the hyperkalemic event, prior to the event, and after the event for each patient. All 12-lead ECG signals were matched with an electrolyte test within 2 hours of each ECG to form a data set. We then analyzed the ECG signals with a duration of 2 seconds and a segment composed of 1400 samples. The data set was randomly divided into the training set, validation set, and test set according to the ratio of 6:2:2 percent. The proposed noninvasive screening tool used a deep learning model that can express the complex and cyclic rhythm of cardiac activity. The deep learning model consists of convolutional and pooling layers for noninvasive screening of the serum potassium level from an ECG signal. To extract an optimal single-lead ECG, we evaluated the performances of the proposed deep learning model for each lead including lead I, II, and V1-V6.

Results

The proposed noninvasive screening tool using a single-lead ECG shows high performances with F1 scores of 100%, 96%, and 95% for the training set, validation set, and test set, respectively. The lead II signal was shown to have the highest performance among the ECG leads.

Conclusions

We developed a novel method for noninvasive screening of hyperkalemia using a single-lead ECG signal, and it can be used as a helpful tool in emergency medicine.

Clinical and electrocardiogram presentations of patients with high serum potassium concentrations within emergency settings: a prospective study

BACKGROUND

Elevated potassium level is a common and reversible peri-arrest condition. Diagnosis and management of hyperkalemia in a short time is critical, where electrocardiogram (ECG) alterations might be helpful. We aimed to investigate the role of clinical features and ECGs in early diagnosing and treating hyperkalemia.

METHODS

Prospectively, adult patients who presented to the emergency department (ED) from July 2019 to March 2020 with hyperkalemia (serum potassium ≥5.5mmol/L) were included. History was obtained, and laboratory investigations and ECGs were performed at the presentation and before initiating hyperkalemia therapy. Hyperkalemia severity was divided into mild (5.5-5.9mmol/L), moderate (6.0-6.4mmol/L), and severe (≥6.5mmol/L). A cardiologist and emergency physician blinded to laboratory values, study design, and patients' diagnoses interpreted ECGs and presenting symptoms independently to predict hyperkalemia.

RESULTS

Sixty-seven hyperkalemic patients with a mean (±SD) serum potassium level of 6.5±0.7mmol/L were included in this study. The mean age was 63.9±15.1, and 58.2% were females. Hyperkalemia was mild in 10.4%, moderate in 40.3%, and severe in 49.3%. Almost two thirds of patients (71.6%) had hypertension, 67.2% diabetes, and 64.2% chronic kidney disease. About one-quarter of patients (22.4%) were asymptomatic, while fatigue (46.3%), dyspnea (28.4%), and nausea/vomiting (20.9%) were the most common presenting symptoms. Normal ECGs were observed in 25.4% of patients, while alterations in 74.6%. Atrial fibrillation (13.4%), peaked T wave (11.9%), widened QRS (11.9%), prolonged PR interval (10.5%), and flattening P wave (10.5%) were the most common. Peaked T wave was significantly more common in severe hyperkalemia (87.5%) than in mild and moderate hyperkalemia (12.5%, 0.0%, respectively) (p=0.041). The physicians' sensitivities for predicting hyperkalemia were 35.8% and 28.4%, improved to 51.5% and 42.4%, respectively, when limiting the analyses to severe hyperkalemia. The mean (±SD) time to initial hyperkalemia treatment was 63.8±31.5 min. Potassium levels were positively correlated with PR interval (r=0.283, p=0.038), QRS duration (r=0.361, p=0.003), peaked T wave (r=0.242, p=0.041), and serum levels of creatinine (r=0.347, p=0.004), BUN (r=0.312, p=0.008), and CK (r=0.373, p=0.039).

CONCLUSIONS

The physicians' abilities to predict hyperkalemia based on ECG and symptoms were poor. ECG could not be solely relied on, and serum potassium tests should be conducted for accurate diagnosis.

Interobserver Variability Among Experienced Electrocardiogram Readers To Diagnose Acute Thrombotic Coronary Occlusion In Patients with Out of Hospital Cardiac Arrest: Impact of Metabolic Milieu and Angiographic Culprit

OBJECTIVES

We sought to evaluate interobserver concordance among experienced electrocardiogram (ECG) readers in predicting acute thrombotic coronary occlusion (ATCO) in the context of abnormal metabolic milieu (AMM) following resuscitated out of hospital cardiac arrest (OHCA).

METHODS

OHCA patients with initial shockable rhythm who underwent invasive coronary angiography (ICA) were included. AMM was defined as one of: pH < 7.1, lactate > 2 mmol/L, serum potassium < 2.8 or > 6.0 mEq/L. The initial ECG following ROSC but prior to ICA was adjudicated by 2 experienced readers using classic ST elevation myocardial infarction [STEMI] and expanded criteria and their combination to predict ATCO on ICA.

RESULTS

152 consecutive patients (mean age 58 years, 76% male) met inclusion criteria. AMM was present in 77%; and 42% had ATCO on ICA. Sensitivity, specificity, PPV, NPV using classic STEMI criteria were 50%, 98%, 94%, 72% (c-statistic 0.74); whereas for combined (STEMI + expanded) criteria they were 69%, 88%, 81%, 79% respectively (c-statistic 0.79). Inter-observer agreement (kappa) was 0.7 for classic STEMI criteria, and 0.66 for combined criteria. Agreement between readers was consistently higher when ATCO was absent and with NMM (kappa 0.78), but lower in AMM (kappa 0.6).

CONCLUSIONS

Despite experienced ECG readers, there was only modest overall concordance in predicting ATCO in the context of resuscitated OHCA. Significant interobserver variations were noted dependent on metabolic milieu and angiographic ATCO. These observations fundamentally question the role of the 12-lead ECG as primary triaging tool for early angiography among patients with OHCA.

3D Printed Ion-Selective Membranes and Their Translation into Point-of-Care Sensors

This technical note describes a method for fabricating ion-selective membranes (ISMs) for use in potentiometric sensing by using 3D printing technology. Here, we demonstrate the versatility of this approach by fabricating ISMs and investigating their performance in both liquid-contact and solid-contact ion-selective electrode (ISE) configurations. Using 3D printed ISMs resulted in highly stable (drift of ∼17 μV/h) and highly reproducible (<1 mV deviation) measurements. Furthermore, we show the seamless translation of these membranes into reliable, carbon fiber- and paper-based potentiometric sensors for applications at the point-of-care. To highlight the modifiability of this approach, we fabricated sensors for bilirubin, an important biomarker of liver health; benzalkonium, a common preservative used in the pharmaceutical industry; and potassium, an important blood electrolyte. The ability to mass produce sensors using 3D printing is an attractive advantage over conventional methods, while also decreasing the time and cost associated with sensor fabrication.

Dispelling myths and misconceptions about the treatment of acute hyperkalemia

Hyperkalemia represents a widespread and potentially lethal condition that affects millions of people across their lives. Despite the prevalence and severity of the condition, there are no consensus guidelines on the treatment of hyperkalemia or even a standard definition. Herein, we provide a succinct review of what we believe to be the most significant misconceptions encountered in the emergency care of hyperkalemia, examine current available literature, and discuss practical points on several modalities of hyperkalemia treatment. Additionally, we review the pathophysiology of the electrocardiographic effects of hyperkalemia and how intravenous calcium preparations can antagonize these effects. We conclude each section with recommendations to aid emergency physicians in making safe and efficacious choices for the treatment of acute hyperkalemia.

The application of deep learning in electrocardiogram: Where we came from and where we should go?

Electrocardiogram (ECG) is a commonly-used, non-invasive examination recording cardiac voltage versus time traces over a period. Deep learning technology, a robust artificial intelligence algorithm, can imitate the data processing patterns of the human brain, and it has experienced remarkable success in disease screening, diagnosis, and prediction. Compared with traditional machine learning, deep learning algorithms possess more powerful learning capabilities and can automatically extract features without extensive data pre-processing or hand-crafted feature extraction, which makes it a suitable tool to analyze complex structures of high-dimensional data. With the advances in computing power and digitized data availability, deep learning provides us an opportunity to improve ECG data interpretation with higher efficacy and accuracy and, more importantly, expand the original functions of ECG. The application of deep learning has led us to stand at the edge of ECG innovation and will potentially change the current clinical monitoring and management strategies. In this review, we introduce deep learning technology and summarize its advantages compared with traditional machine learning algorithms. Moreover, we provide an overview on the current application of deep learning in ECGs, with a focus on arrhythmia (especially atrial fibrillation during normal sinus rhythm), cardiac dysfunction, electrolyte imbalance, and sleep apnea. Last but not least, we discuss the current challenges and prospect directions for the following studies.

Electrocardiographic changes in the acute hyperkalaemia produced by intragastric KCl load in rats

NEW FINDINGS

What is the central question of this study? This study presents a new model for studying the rapid onset of severe, acute hyperkalaemia in rats with intact kidney function by administering an intragastric KCl load. What is the main finding and its importance? This new model of intragastric KCl load produces a reliable and reproducible model for studying the rapid onset of severe, acute hyperkalaemia in rats with intact kidney function. We report unprecedented rapid changes (30 min) in ECG, blood pressure and various arterial blood analyses with this new model, providing a solid foundation for future experiments in this field.

ABSTRACT

A variety of animal models have been proposed to study hyperkalaemia, but most of them have meaningful limitations when the goal is to study the effect of potassium overload on healthy kidneys. In this study, we aimed to introduce a new approach for induction of hyperkalaemia in a reliable and reproducible animal model. We used intragastric administration of potassium chloride [KCl 2.3 M, 10 ml/(kg body weight)] to male Holtzman rats (300-350 g) to induce hyperkalaemia. The results showed that this potassium load can temporarily overwhelm the renal and extrarenal handling of this ion, causing an acute and severe hyperkalaemia that can be useful to study the effect of potassium imbalance in a variety of scenarios. Severe hyperkalaemia (>8 meqiv/l) and very profound ECG alterations, characterized by lengthening waves and intervals, were seen as early as 30 min after intragastric administration of KCl in rats. In addition, a transient increase in arterial blood pressure and time-dependent bradycardia were also seen after the KCl administration. No metabolic acidosis was present in the animals, and the potassium ion did not increase proportionally to chloride ion in the blood, leading to an increased anion gap. In conclusion, the results suggest that intragastric KCl loading is a reliable model to promote rapid and severe hyperkalaemia that can be used for further research on this topic.

Deep learning and the electrocardiogram: review of the current state-of-the-art

In the recent decade, deep learning, a subset of artificial intelligence and machine learning, has been used to identify patterns in big healthcare datasets for disease phenotyping, event predictions, and complex decision making. Public datasets for electrocardiograms (ECGs) have existed since the 1980s and have been used for very specific tasks in cardiology, such as arrhythmia, ischemia, and cardiomyopathy detection. Recently, private institutions have begun curating large ECG databases that are orders of magnitude larger than the public databases for ingestion by deep learning models. These efforts have demonstrated not only improved performance and generalizability in these aforementioned tasks but also application to novel clinical scenarios. This review focuses on orienting the clinician towards fundamental tenets of deep learning, state-of-the-art prior to its use for ECG analysis, and current applications of deep learning on ECGs, as well as their limitations and future areas of improvement.

An Evidence-Based Narrative Review of the Emergency Department Management of Acute Hyperkalemia

BACKGROUND

The normal range for potassium is within narrow limits. Hyperkalemia is an electrolyte disorder that frequently affects patients in the emergency department (ED), and can result in significant morbidity and mortality if not identified and treated rapidly.

OBJECTIVE

This article provides an evidence-based narrative review of the management of hyperkalemia, with focused updates for the emergency clinician.

METHODS

We searched in MEDLINE, EMBASE, Web of Science, and Scopus databases for articles in English published in peer-reviewed journals and indexed up until May 2020. We used multiple search terms, including hyperkalemia, potassium, acute hyperkalemia, emergency department, dyskalemia, potassium disorders, kidney disease, epidemiology, electrolyte disturbance, severe hyperkalemia, and emergency management.

DISCUSSION

In the ED, interventions aimed to protect patients from the immediate dangers of elevated serum potassium are divided into the following: stabilizing cardiac membrane potentials, reducing serum potassium levels through shift from the extracellular fluid to intracellular fluid, and elimination of potassium through excretion via urinary or fecal excretion. Calcium is widely recommended to stabilize the myocardial cell membrane, but additional research is necessary to establish criteria for use, dosages, and preferred solutions. Redistribution of potassium ions from the bloodstream into the cells is based on intravenous insulin or nebulized β2-agonists.

CONCLUSIONS

Hyperkalemia is a frequent electrolyte disorder in the ED. Because of the risk of fatal dysrhythmia due to cardiac membrane instability, hyperkalemia is a medical emergency. There is a lack of scientific evidence on the optimal management of hyperkalemia and more research is needed to establish optimal strategies to manage acute hyperkalemia in the emergency department.

Quantification and classification of potassium and calcium disorders with the electrocardiogram: What do clinical studies, modeling, and reconstruction tell us?

Diseases caused by alterations of ionic concentrations are frequently observed challenges and play an important role in clinical practice. The clinically established method for the diagnosis of electrolyte concentration imbalance is blood tests. A rapid and non-invasive point-of-care method is yet needed. The electrocardiogram (ECG) could meet this need and becomes an established diagnostic tool allowing home monitoring of the electrolyte concentration also by wearable devices. In this review, we present the current state of potassium and calcium concentration monitoring using the ECG and summarize results from previous work. Selected clinical studies are presented, supporting or questioning the use of the ECG for the monitoring of electrolyte concentration imbalances. Differences in the findings from automatic monitoring studies are discussed, and current studies utilizing machine learning are presented demonstrating the potential of the deep learning approach. Furthermore, we demonstrate the potential of computational modeling approaches to gain insight into the mechanisms of relevant clinical findings and as a tool to obtain synthetic data for methodical improvements in monitoring approaches.

A method comparison study of a point-of-care blood gas analyser with a laboratory auto-analyser for the determination of potassium concentrations during hyperkalaemia in patients with kidney disease

Introduction

Hyperkalaemia is a common electrolyte disorder that may cause life-threatening cardiac arrythmias. We aimed to determine the agreement of potassium concentrations between GEM premier 3500 point-of-care blood gas analyser (POC-BGA) and Roche Cobas 6000 c501 auto-analyser in patients with hyperkalaemia.

Methods

A prospective, cross-sectional study of all consecutive adult patients referred to the Renal Unit with a serum potassium concentration ≥ 5.5 mmol/L was performed. A total of 59 paired venous blood samples were included in the final statistical analysis. Passing-Bablok regression and Bland Altman analysis were used to compare the two methods.

Results

The median laboratory auto-analyser potassium concentration was 6.1 (5.9-7.1) mmol/L as compared to the POC-BGA potassium concentration of 5.7 (5.5-6.8) mmol/L with a mean difference of - 0.43 mmol/L and 95% upper and lower limits of agreement of 0.35 mmol/L and - 1.21 mmol/L, respectively. Regression analysis revealed proportional systematic error. Test for linearity did not indicate significant deviation (P = 0.297).

Conclusion

Although regression analysis indicated proportional systematic error, on Bland Altman analysis, the mean difference appeared to remain relatively constant across the potassium range that was evaluated. Therefore, in patients presenting to the emergency department with a clinical suspicion of hyperkalaemia, POC-BGA potassium concentrations may be considered a surrogate for laboratory auto-analyser measurements once clinicians have been cautioned about this difference.

Acute Kidney Injury in Active Wars and Other Man-Made Disasters

Acute kidney injury (AKI) is frequent during wars and other man-made disasters, and contributes significantly to the overall death toll. War-related AKI may develop as a result of polytrauma, traumatic bleeding and hypovolemia, chemical and airborne toxin exposure, and crush syndrome. Thus, prerenal, intrinsic renal, or postrenal AKI may develop at the battlefield, in field hospitals, or tertiary care centers, resulting not only from traumatic, but also nontraumatic, etiologies. The prognosis usually is unfavorable because of systemic and polytrauma-related complications and suboptimal therapeutic interventions. Measures for decreasing the risk of AKI include making preparations for foreseeable disasters, and early management of polytrauma-related complications, hypovolemia, and other pathogenetic mechanisms. Transporting casualties initially to field hospitals, and afterward to higher-level health care facilities at the earliest convenience, is critical. Other man-made disasters also may cause AKI; however, the number of patients is mostly lower and treatment possibilities are broader than in war. If there is no alternative other than prolonged field care, the medical community must be prepared to offer health care and even perform dialysis in austere conditions, which in that case, is the only option to decrease the death toll resulting from AKI.

Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram

Importance

For patients with chronic kidney disease (CKD), hyperkalemia is common, associated with fatal arrhythmias, and often asymptomatic, while guideline-directed monitoring of serum potassium is underused. A deep-learning model that enables noninvasive hyperkalemia screening from the electrocardiogram (ECG) may improve detection of this life-threatening condition.

Objective

To evaluate the performance of a deep-learning model in detection of hyperkalemia from the ECG in patients with CKD.

Design, Setting, and Participants

A deep convolutional neural network (DNN) was trained using 1 576 581 ECGs from 449 380 patients seen at Mayo Clinic, Rochester, Minnesota, from 1994 to 2017. The DNN was trained using 2 (leads I and II) or 4 (leads I, II, V3, and V5) ECG leads to detect serum potassium levels of 5.5 mEq/L or less (to convert to millimoles per liter, multiply by 1) and was validated using retrospective data from the Mayo Clinic in Minnesota, Florida, and Arizona. The validation included 61 965 patients with stage 3 or greater CKD. Each patient had a serum potassium count drawn within 4 hours after their ECG was recorded. Data were analyzed between April 12, 2018, and June 25, 2018.

Exposures

Use of a deep-learning model.

Main Outcomes and Measures

Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity, with serum potassium level as the reference standard. The model was evaluated at 2 operating points, 1 for equal specificity and sensitivity and another for high (90%) sensitivity.

Results

Of the total 1 638 546 ECGs, 908 000 (55%) were from men. The prevalence of hyperkalemia in the 3 validation data sets ranged from 2.6% (n = 1282 of 50 099; Minnesota) to 4.8% (n = 287 of 6011; Florida). Using ECG leads I and II, the AUC of the deep-learning model was 0.883 (95% CI, 0.873-0.893) for Minnesota, 0.860 (95% CI, 0.837-0.883) for Florida, and 0.853 (95% CI, 0.830-0.877) for Arizona. Using a 90% sensitivity operating point, the sensitivity was 90.2% (95% CI, 88.4%-91.7%) and specificity was 63.2% (95% CI, 62.7%-63.6%) for Minnesota; the sensitivity was 91.3% (95% CI, 87.4%-94.3%) and specificity was 54.7% (95% CI, 53.4%-56.0%) for Florida; and the sensitivity was 88.9% (95% CI, 84.5%-92.4%) and specificity was 55.0% (95% CI, 53.7%-56.3%) for Arizona.

Conclusions and Relevance

In this study, using only 2 ECG leads, a deep-learning model detected hyperkalemia in patients with renal disease with an AUC of 0.853 to 0.883. The application of artificial intelligence to the ECG may enable screening for hyperkalemia. Prospective studies are warranted.

Electrocardiographic T wave alterations and prediction of hyperkalemia in patients with acute kidney injury

Electrocardiographic (ECG) alterations are common in hyperkalemic patients. While the presence of peaked T waves is the most frequent ECG alteration, reported findings on ECG sensitivity in detecting hyperkalemia are conflicting. Moreover, no studies have been conducted specifically in patients with acute kidney injury (AKI). We used the best subset selection and cross-validation methods [via linear and logistic regression and leave-one-out cross-validation (LOOCV)] to assess the ability of T waves to predict serum potassium levels or hyperkalemia (defined as serum potassium ≥ 5.5 mEq/L). We included the following clinical variables as a candidate for the predictive models: peaked T waves, T wave maximum amplitude, T wave/R wave maximum amplitude ratio, age, and indicator variates for oliguria, use of ACE-inhibitors, sartans, mineralocorticoid receptor antagonists, and loop diuretics. Peaked T waves poorly predicted the serum potassium levels in both full and test sample (R² = 0.03 and R² = 0.01, respectively), and also poorly predicted hyperkalemia. The selection algorithm based on Bayesian information criterion identified T wave amplitude and use of loop diuretics as the best subset of variables predicting serum potassium. Nonetheless, the model accuracy was poor in both full and test sample [root mean square error (RMSE) = 0.96 mEq/L and adjR² = 0.08 and RMSE = 0.97 mEq/L, adjR² = 0.06, respectively]. T wave amplitude and the use of loop diuretics had also poor accuracy in predicting hyperkalemia in both full and test sample [area-under-curve (AUC) at receiver-operator curve (ROC) analysis 0.74 and AUC 0.72, respectively]. Our findings show that, in patients with AKI, electrocardiographic changes in T waves are poor predictors of serum potassium levels and of the presence of hyperkalemia.

A Deep-Learning Algorithm (ECG12Net) for Detecting Hypokalemia and Hyperkalemia by Electrocardiography: Algorithm Development

Background

The detection of dyskalemias—hypokalemia and hyperkalemia—currently depends on laboratory tests. Since cardiac tissue is very sensitive to dyskalemia, electrocardiography (ECG) may be able to uncover clinically important dyskalemias before laboratory results.

Objective

Our study aimed to develop a deep-learning model, ECG12Net, to detect dyskalemias based on ECG presentations and to evaluate the logic and performance of this model.

Methods

Spanning from May 2011 to December 2016, 66,321 ECG records with corresponding serum potassium (K⁺) concentrations were obtained from 40,180 patients admitted to the emergency department. ECG12Net is an 82-layer convolutional neural network that estimates serum K⁺ concentration. Six clinicians—three emergency physicians and three cardiologists—participated in human-machine competition. Sensitivity, specificity, and balance accuracy were used to evaluate the performance of ECG12Net with that of these physicians.

Results

In a human-machine competition including 300 ECGs of different serum K+ concentrations, the area under the curve for detecting hypokalemia and hyperkalemia with ECG12Net was 0.926 and 0.958, respectively, which was significantly better than that of our best clinicians. Moreover, in detecting hypokalemia and hyperkalemia, the sensitivities were 96.7% and 83.3%, respectively, and the specificities were 93.3% and 97.8%, respectively. In a test set including 13,222 ECGs, ECG12Net had a similar performance in terms of sensitivity for severe hypokalemia (95.6%) and severe hyperkalemia (84.5%), with a mean absolute error of 0.531. The specificities for detecting hypokalemia and hyperkalemia were 81.6% and 96.0%, respectively.

Conclusions

A deep-learning model based on a 12-lead ECG may help physicians promptly recognize severe dyskalemias and thereby potentially reduce cardiac events.

ECG alterations suggestive of hyperkalemia in normokalemic versus hyperkalemic patients

Background

In periarrest situations and during resuscitation it is essential to rule out reversible causes. Hyperkalemia is one of the most common, reversible causes of periarrest situations. Typical electrocardiogram (ECG) alterations may indicate hyperkalemia. The aim of our study was to compare the prevalence of ECG alterations suggestive of hyperkalemia in normokalemic and hyperkalemic patients.

Methods

170 patients with normal potassium (K⁺) levels and 135 patients with moderate (serum K⁺ = 6.0–7.0 mmol/l) or severe (K⁺ > 7.0 mmol/l) hyperkalemia, admitted to the Department of Emergency Medicine at the Somogy County Kaposi Mór General Hospital, were selected for this retrospective, cross-sectional study. ECG obtained upon admission were analyzed by two emergency physicians, independently, blinded to the objectives of the study. Statistical analysis was performed using SPSS22 software. χ² test and Fischer exact tests were applied.

Results

24% of normokalemic patients and 46% of patients with elevated potassium levels had some kind of ECG alteration suggestive of hyperkalemia. Wide QRS (31.6%), peaked T-waves (18.4%), Ist degree AV-block (18.4%) and bradycardia (18.4%) were the most common and significantly more frequent ECG alterations suggestive of hyperkalemia in severely hyperkalemic patients compared with normokalemic patients (8.2, 4.7, 7.1 and 6.5%, respectively). There was no significant difference between the frequency of ECG alterations suggestive of hyperkalemia in normokalemic and moderately hyperkalemic patients. Upon examining ECG alterations not typically associated with hyperkalemia, we found that prolonged QTc was the only ECG alteration which was significantly more prevalent in both patients with moderate (17.5%) and severe hyperkalemia (21.1%) compared to patients with normokalemia (5.3%).

Conclusions

A minority of patients with normal potassium levels may also exhibit ECG alterations considered to be suggestive of hyperkalemia, while more than half of the patients with hyperkalemia do not have ECG alterations suggesting hyperkalemia. These results imply that treatment of hyperkalemia in the prehospital setting should be initiated with caution. Multiple ECG alterations, however, should draw attention to potentially life threatening conditions.

Can physicians detect hyperkalemia based on the electrocardiogram?

OBJECTIVE

Although there is no consensus on how to use an electrocardiogram (ECG) in patients with hyperkalemia, physicians often obtain it in the acute setting when diagnosing and treating hyperkalemia. The objective of this study is to evaluate if physicians are able to detect hyperkalemia based on the ECG.

METHODS

The study was conducted at a large county hospital with a population of end stage renal disease (ESRD) patients who received hemodialysis (HD) solely on an emergent basis. Five hundred twenty eight ECGs from ESRD patients were evaluated. The prevalence of hyperkalemia was approximately 60% in this cohort, with at least half of them in the severe hyperkalemia range (K ≥ 6.5 mEq/L).

RESULTS

The mean sensitivity and specificity of the emergency physicians detecting hyperkalemia were 0.19 (± 0.16) and 0.97(± 0.04) respectively. The mean positive predictive value of evaluators for detecting hyperkalemia was 0.92 (±0.13) and the mean negative predictive value was 0.46 (± 0.05). In severe hyperkalemia (K ≥ 6.5 mEq/L), the mean sensitivity improved to 0.29 (± 0.20), while specificity decreased to 0.95 (±0.07).

CONCLUSION

An ECG is not a sensitive method of detecting hyperkalemia and should not be relied upon to rule it out. However, the ECG has a high specificity for detecting hyperkalemia and could be used as a rule in test.

Prise en charge de l’hyperkaliémie aux urgences

L’hyperkaliémie est l’un des désordres hydroélectrolytiques les plus fréquemment rencontrés aux urgences. Les étiologies principales sont l’insuffisance rénale aiguë ou chronique, le diabète et l’insuffisance cardiaque. L’hyperkaliémie aiguë peut être une urgence vitale, car elle est potentiellement létale du fait du risque d’arythmie cardiaque. Sa prise en charge aux urgences manque actuellement de recommandations claires en ce qui concerne le seuil d’intervention et les thérapeutiques à utiliser. Les thérapeutiques couramment appliquées sont fondées sur un faible niveau de preuve, et leurs effets secondaires sont mal connus. Des études supplémentaires sont nécessaires pour évaluer l’utilisation de ces traitements et celle de nouveaux traitements potentiellement prometteurs. Nous faisons ici une mise au point sur les données connues en termes d’épidémiologie, de manifestations cliniques et électrocardiographiques, et des différentes thérapeutiques qui peuvent être proposées dans la prise en charge de l’hyperkaliémie aux urgences.

Current treatment and unmet needs of hyperkalaemia in the emergency department

Hyperkalaemia is a common electrolyte abnormality and can cause life-threatening cardiac arrhythmia. Even though it is common in patients with diabetes, heart failure, and kidney disease, there is poor consensus over its definition and wide variability in its treatment. Medications used to treat hyperkalaemia in the emergent setting do not have robust efficacy and safety data to guide treatment leading to mismanagement due to poor choice of some agents or inappropriate dosing of others. Moreover, the medications used in the emergent setting are at best temporizing measures, with dialysis being the definitive treatment. New and old k binder therapies provide means to excrete potassium, but their roles are unclear in the emergent setting. Electrocardiograms are the corner stones of hyperkalaemia management; however, recent studies show that they might manifest abnormalities infrequently, even in severe hyperkalaemia, thus questioning their role. With an aging population and a rise in rates of heart and kidney failure, hyperkalaemia is on the rise, and there is a need, now more than ever, to understand the efficacy and safety of the current medications and to develop newer ones.

Quantitative Evaluation of the Relationship between T-Wave-Based Features and Serum Potassium Level in Real-World Clinical Practice

BACKGROUND

Proper management of hyperkalemia that leads to fatal cardiac arrhythmia has become more important because of the increased prevalence of hyperkalemia-prone diseases. Although T-wave changes in hyperkalemia are well known, their usefulness is debatable. We evaluated how well T-wave-based features of electrocardiograms (ECGs) are correlated with estimated serum potassium levels using ECG data from real-world clinical practice.

METHODS

We collected ECGs from a local ECG repository (MUSE™) from 1994 to 2017 and extracted the ECG waveforms. Of about 1 million reports, 124,238 were conducted within 5 minutes before or after blood collection for serum potassium estimation. We randomly selected 500 ECGs and two evaluators measured the amplitude (T-amp) and right slope of the T-wave (T-right slope) on five lead waveforms (V3, V4, V5, V6, and II). Linear correlations of T-amp, T-right slope, and their normalized feature (T-norm) with serum potassium levels were evaluated using Pearson correlation coefficient analysis.

RESULTS

Pearson correlation coefficients for T-wave-based features with serum potassium between the two evaluators were 0.99 for T-amp and 0.97 for T-right slope. The coefficient for the association between T-amp, T-right slope, and T-norm, and serum potassium ranged from -0.22 to 0.02. In the normal ECG subgroup (normal ECG or otherwise normal ECG), there was no correlation between T-wave-based features and serum potassium level.

CONCLUSIONS

T-wave-based features were not correlated with serum potassium level, and their use in real clinical practice is currently limited.

Electrocardiographic findings in 130 hospitalized neonatal calves with diarrhea and associated potassium balance disorders

Background

Hyperkalemia in neonatal diarrheic calves can potentially result in serious cardiac conduction abnormalities and arrhythmias.

Objectives

To document electrocardiographic (ECG) findings and the sequence of ECG changes that are associated with increasing plasma potassium concentrations (cK⁺) in a large population of neonatal diarrheic calves.

Animals

One hundred and thirty neonatal diarrheic calves (age ≤21 days).

Methods

Prospective observational study involving calves admitted to a veterinary teaching hospital.

Results

Hyperkalemic calves (cK⁺: 5.8‐10.2, blood pH: 6.55‐7.47) had significantly (P < .05) longer QRS durations as well as deeper S wave, higher T wave, and higher ST segment amplitudes in lead II than calves, which had both venous blood pH and cK⁺ within the reference range. The first ECG changes in response to an increase in cK⁺ were an increase in voltages of P, Ta, S, and T wave amplitudes. Segmented linear regression indicated that P wave amplitude decreased when cK⁺ >6.5 mmol/L, S wave amplitude voltage decreased when cK⁺ >7.4 mmol/L, QRS duration increased when cK⁺ >7.8 mmol/L, J point amplitude increased when cK⁺ >7.9 mmol/L, and ST segment angle increased when cK⁺ >9.1 mmol/L. P wave amplitude was characterized by a second common break point at cK⁺ = 8.2 mmol/L, above which value the amplitude was 0.

Conclusions and Clinical Importance

Hyperkalemia in neonatal diarrheic calves is associated with serious cardiac conduction abnormalities. In addition to increased S and T wave amplitude voltages, alterations of P and Ta wave amplitudes are early signs of hyperkalemia, which is consistent with the known sensitivity of atrial myocytes to increased cK⁺.

Computer-assisted image processing 12 lead ECG model to diagnose hyperkalemia

BACKGROUND

We sought to develop an improved 12 lead ECG model to diagnose hyperkalemia by use of traditional and novel parameters.

METHODS

We retrospectively analyzed ECGs in consecutive hyperkalemic patients (serum potassium (K)>5.3mEq/L) by blinded investigators with normokalemic ECGs as internal controls. Potassium levels were modeled using general linear mixed models followed by refit with standardized variables. Optimum sensitivity and specificity were determined using cut point analysis of ROC-AUC.

RESULTS

The training set included 236 ECGs (84 patients) and validation set 97 ECGs (23 patients). Predicted K=(5.2354)+(0.03434*descending T slope)+(-0.2329*T width)+(-0.9652*reciprocal of new QRS width>100msec). ROC-AUC in the validation set was 0.78 (95% CI 0.69-0.88). Maximum specificity of the model was 84% for K>5.91 with sensitivity of 63%.

CONCLUSION

ECG model incorporating T-wave width, descending T-wave slope and new QRS prolongation improved hyperkalemia diagnosis over traditional ECG analysis.

Electrophysiological and clinical consequences of hyperkalemia

Despite the potentially life-threatening consequences of hyperkalemia, symptoms are often absent or mild. However, when hyperkalemia has been recognized, evaluation of vital signs is essential for determining hemodynamic stability and identifying the presence of cardiac arrhythmias related to the hyperkalemia. Quite commonly, and depending on the severity and rapidity of onset, hyperkalemia may be associated with substantial electrocardiographic (EKG) changes that can lead to death if proper interventions are not instituted. Through its effects on the resting membrane potential and threshold potential of excitable cells, hyperkalemia is a potentially life-threatening disorder. Symptoms and physical examination findings are often absent. Once identified, the entire clinical picture must be taken into account, including an assessment of hemodynamic stability, the presence of other electrolyte abnormalities, and an EKG evaluation. While there is a typical progression of EKG findings based on hyperkalemia severity, EKG manifestations are myriad and their evolution may be unpredictable.

Acute hyperkalemia leading to flaccid paralysis: a review of hyperkalemic manifestations

Hyperkalemia can present with a spectrum of clinical manifestations with progressive EKG changes and life-threatening arrhythmias. Although no formal guidelines exist as to when to initiate treatment for hyperkalemia, it is generally recommended in clinically symptomatic patients with or without EKG changes. Timely diagnosis and reversal can relieve symptoms and prevent life-threatening arrhythmias. We review the EKG changes associated with hyperkalemia and management principles along with an example of a case of severe hyperkalemia resulting in arrhythmia and flaccid paralysis.

Noninvasive potassium determination using a mathematically processed ECG: proof of concept for a novel "blood-less, blood test"

OBJECTIVE

To determine if ECG repolarization measures can be used to detect small changes in serum potassium levels in hemodialysis patients.

PATIENTS AND METHODS

Signal-averaged ECGs were obtained from standard ECG leads in 12 patients before, during, and after dialysis. Based on physiological considerations, five repolarization-related ECG measures were chosen and automatically extracted for analysis: the slope of the T wave downstroke (T right slope), the amplitude of the T wave (T amplitude), the center of gravity (COG) of the T wave (T COG), the ratio of the amplitude of the T wave to amplitude of the R wave (T/R amplitude), and the center of gravity of the last 25% of the area under the T wave curve (T4 COG) (Fig. 1).

RESULTS

The correlations with potassium were statistically significant for T right slope (P<0.0001), T COG (P=0.007), T amplitude (P=0.0006) and T/R amplitude (P=0.03), but not T4 COG (P=0.13). Potassium changes as small as 0.2mmol/L were detectable.

CONCLUSION

Small changes in blood potassium concentrations, within the normal range, resulted in quantifiable changes in the processed, signal-averaged ECG. This indicates that non-invasive, ECG-based potassium measurement is feasible and suggests that continuous or remote monitoring systems could be developed to detect early potassium deviations among high-risk patients, such as those with cardiovascular and renal diseases. The results of this feasibility study will need to be further confirmed in a larger cohort of patients.

Evaluation of potassium levels before hemodialysis access procedures

Few prospective studies have looked at the incidence of hyperkalemia in outpatient hemodialysis access procedures. Our study prospectively evaluated 167 procedures using a preadmit venous blood gas (VBG) and found that 14.3% had moderate or severe hyperkalemia. When the individual procedures were analyzed it was found that 38% of malfunctioning tunneled dialysis catheter (TDC) patients, 20% of new start TDC patients, 22% of thrombectomy patients, and only 5.8% of the angioplasty patients had moderate or severe hyperkalemia. We have changed our practice and now monitor the preprocedure potassium in all but the routine angioplasty patients and treat with the protocol described in the body of the article.

Independent determinants for presence and degree of left ventricular systolic dyssynchrony in treatment-naive patients with hypertension

OBJECTIVE

Prevalence of left ventricular systolic dyssynchrony (LVSD) is over 40% in treatment-naive patients with hypertension and it improves after chronic antihypertensive treatment. These findings might support the hypothesis that blood pressure (BP), BP-derived parameters, central BP, or arterial stiffness would contribute to LVSD. Therefore, we aimed to investigate possible factors associated with LVSD in treatment-naive patients with hypertension.

METHODS

The study groups consisted of 266 treatment-naive hypertensive patients who underwent anthropometric, clinical, laboratory, echocardiographic, arterial stiffness, central blood pressure, and 24-h ambulatory blood pressure monitoring evaluations. Echocardiographic measurement was recorded as follows: peak systolic velocity (Sa, subclinical left ventricular systolic function), peak early diastolic and late diastolic velocity at the mitral annulus (Ea and Aa, respectively), mitral E/Ea ratio (subclinical left ventricular diastolic function), standard deviation of time from ECG Q to systolic peak velocity of 12 left ventricular segments (Ts-SD12), and maximal difference between peak systolic velocities of any 2 of the 12 segments (Ts-Max). A Ts-SD12 at least 33 or Ts-Max at least 100 ms was regarded as presence of LVSD.

RESULTS

Patients were divided into those without LVSD (group 1, n = 151, 56.8%) and those with LVSD (group 2, n = 115, 43.2%). Group 2 had higher E/Ea and high-density lipoprotein and lower Sa and triglyceride than group 1. On multivariate analysis, Sa was independently and inversely associated with the presence of LVSD [odds ratio (OR) 0.67, 95% confidence interval (CI) 0.48-0.93, P = 0.018]. The linear relationship between variables and degree of LVSD showed that serum potassium levels, E/Ea, and Sa remained significant after multivariate analysis (potassium, β = 0.199, P = 0.006; E/Ea, β = 0.211, P = 0.017; Sa, β = -0.301, P < 0.001 in Ts-SD12 and potassium, β=0.187, P = 0.010; E/Ea, β = 0.234, P = 0.008; Sa, β = -0.322, P < 0.001 in Ts-Max, respectively).

CONCLUSION

Subclinical left ventricular systolic function is independently associated with both the presence and degree of LVSD in treatment-naive hypertensive patients. Subclinical left ventricular diastolic function and serum potassium levels are independently associated with the degree of LVSD. However, arterial stiffness and BP parameters are not determinants.